SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination

Source

Source name: arXiv
Source kind: paper
Authors / organization: Yunhao Liang, Xianqi Cao, Pujun Zhang, Yuan Qu, Yongzhi Qi, Ningxuan Kang, Max Z. J. Shen
Venue: arXiv: cs.AI
Published: 2026-07-30
Collected: 2026-07-31
URL: http://arxiv.org/abs/2607.28488v1
PDF: https://arxiv.org/pdf/2607.28488v1
DOI: UNKNOWN

Plain-Language Brief

The paper proposes a new composite policy model called SCOPE for coordinating decisions in supply chains. Instead of handling decisions separately by different departments and systems, SCOPE combines them into a unified plan that considers the operational couplings between these decisions. The model was tested on urban fresh-retail replenishment using data from two large-scale supply chains, Dingdong and JD.com, and showed better performance than separate optimization methods and common practice-oriented baselines.

What the Document Says

The paper introduces SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination.
SCOPE represents supply-chain entities as tokens, contextualizes them through a shared operational representation, and maps each token type to the corresponding decision interface.
Each decision in SCOPE builds on the partial plan formed by earlier decisions.
The completed plan is evaluated using a shared system-level utility.
SCOPE was tested on urban fresh-retail replenishment with data from Dingdong and JD.com.
SCOPE outperformed methods that optimize each decision stage separately and practice-oriented baselines commonly used in supply-chain operations.

Key Concepts Mentioned

supply-chain AI
coupled policies
end-to-end coordination
composite policy model
urban fresh-retail replenishment
Dingdong
JD.com

Claims Made by the Document

SCOPE leads to more effective end-to-end supply-chain decisions.
Separate optimization of decisions in modern supply chains can lead to stockouts, inventory exposure, and avoidable transportation.

Evidence Used

Results from testing SCOPE on real operational data

Methods / Approach

Composite policy model
Shared operational representation
Decision interfaces
System-level utility

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

The paper does not discuss potential limitations of the proposed approach in detail.

Open Questions

What are the potential challenges in implementing SCOPE on other types of supply chains?
How can SCOPE be adapted to handle dynamic changes and unforeseen circumstances?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper focuses on urban fresh-retail replenishment, so it's important to consider how well the proposed approach might generalize to other types of supply chains.

Raw Abstract / Excerpt

Can supply-chain AI move beyond isolated decision modules toward unified operational planning? A complete replenishment plan specifies which products each location carries, which upstream facility supplies it, how often it is replenished, and how deliveries are routed. These decisions are operationally coupled: the selected assortment changes the demand and load passed to later stages; source assignment and replenishment frequency reshape the delivery requests; and route feasibility and cost, in turn, determine the system value of the earlier choices. Yet in modern supply chains, these decisions are often handled by separate departments and optimized through separate systems, which can lead to stockouts, inventory exposure, and avoidable transportation. We propose SCOPE: Supply-Chain Operations through Coupled Policies for End-to-End Coordination, a composite policy model that represents supply-chain entities as tokens, contextualizes them through a shared operational representation, and maps each token type to the corresponding decision interface. Each decision builds on the partial plan formed by earlier decisions while the completed plan is evaluated using a shared system-level utility. We instantiate this framework in urban fresh-retail replenishment, where service frequency, assortment, capacity pressure, and road-network routing interact strongly, and evaluate it on real operational data from Dingdong and JD.com, two large-scale supply chains operating at different replenishment echelons. Across both settings, SCOPE consistently outperforms methods that optimize each decision stage separately, as well as practice-oriented baselines commonly used in supply-chain operations. These results show that learning and coordinating cross-department operational couplings lead to more effective end-to-end supply-chain decisions.